For years, organic traffic has been the golden KPI, the metric marketers obsessively tracked, the one that reassured us our carefully crafted SEO strategies were working. But the digital landscape is shifting, and the culprit isn’t your keyword strategy, your content team, or even your competitors. It’s artificial intelligence.

As AI-powered search tools start to become more popular, they’re quietly reshaping the way people discover and consume information online. SEMrush even predicts that AI-driven search will fully replace traditional search engines by 2028. That may sound like science fiction, but the early signals are already here, and they’re showing up in your analytics dashboards.

What does this mean for marketers? The playbook we’ve been running for over a decade is being rewritten in real time. Traffic dips are no longer just seasonal fluctuations or the result of algorithm updates, they’re the ripple effects of a fundamental shift in how people find and consume information. And that brings us to the big question: if organic traffic is declining everywhere, what’s really happening under the hood?

The Impact of AI on Organic Traffic

At the heart of this shift is Google’s effort to transform from a search engine into an “answer engine.” AI Overviews, launched in 2024 as part of Google’s Search Generative Experience (SGE), now appear on roughly 13% of queries—more than double from January to March 2025. These summaries leverage machine learning to compile concise, context-aware answers drawn from across the web, often satisfying user intent without the need to click any link. 

Other features like featured snippets, knowledge panels, People Also Ask boxes, and local packs also contribute to the zero‑click dynamic, with nearly 60% of all search queries now ending without a single click.

Some industries have reported organic traffic declines as steep as 15 to 64 percent since AI Overviews were rolled out. 

For marketers who’ve spent years optimizing for long-tail keywords, chasing backlinks, and crafting blog posts to lure visitors, this shift can feel discouraging. But it’s important to remember that it’s not that your efforts are going unnoticed, it’s simply that the playing field has changed.  

Less Traffic, Higher Intent

At first glance, this sounds like a doomsday scenario for SEO. Fewer clicks mean fewer visitors, which means fewer opportunities to engage, nurture, and convert. But here’s the twist: while overall traffic is down, the visitors who do arrive on your site are more intentional than ever.

Casual browsers who might have clicked your link just to skim the answer are being filtered out by AI-generated summaries. The people still coming through are the ones who want more than a quick definition. They’re seeking deeper insights, detailed resources, or product-specific information. And when they land on your site, they’re more primed to take action.

In fact, one study found that visitors driven by large language models (LLMs) are about 4.4 times more likely to convert compared to traditional search visitors. Adobe’s analysis of retail site data during the 2024 holiday season also revealed that visitors coming via AI-driven search stayed 8% longer, visited 12% more pages, and bounced 23% less than those from traditional searches.

So yes, your volume of traffic may be lower, but the quality of leads has the potential to skyrocket.

It’s a tradeoff worth paying attention to. Chasing vanity metrics like sheer visitor numbers might no longer make sense. Instead, success will be measured by how well you capture and serve this smaller but far more valuable audience.

How to Adapt to the Future of AI-Powered Commerce

The landscape may be shifting, but marketers are nothing if not adaptable. This isn’t the end of SEO, it’s just the next evolution of it, so you don’t need to completely rewrite the playbook. Instead, here’s how you can start to rethink your approach and adapt it to the new future of commerce.

1. Start by understanding how these LLMs interpret and talk about your product

The first step in adapting is to understand how LLMs actually process and interpret data. These systems don’t “read” content like a human would; instead, they analyze enormous datasets (product descriptions, technical specifications, schema markup, contextual signals from across the web, etc.) to decide how to surface information. That means accuracy, structure, and consistency matter more than ever. If your product data is incomplete, inconsistent, or overly generic, it’s less likely to be picked up and reflected in AI-generated answers. Optimizing for LLMs is about ensuring your content is machine-readable, semantically clear, and rich enough to stand out in a generative response.

This is where tools purpose-built for the new AI-driven search landscape can make a difference, like Akeneo’s AI Discovery Optimization feature, which helps businesses enrich and structure their product information in ways that align with how LLMs interpret data. By bridging the gap between human-friendly product storytelling and machine-friendly precision, our tool increases the likelihood that your products will be correctly understood, represented, and recommended in AI-powered search experiences. In short, it equips you not just to adapt to the shift but to thrive in it.

2. Incorporate measuring referral traffic from LLMs into your KPIs

The next step is to rethink the way you measure success. Large language models often act as intermediaries, surfacing and contextualizing your content within their own responses before a user ever clicks through. That means valuable touchpoints with your brand are happening outside the walls of your website, and if you’re only looking at conventional web analytics, you’re missing a big part of the picture. 

Start by broadening your reporting to include traffic from AI-powered discovery channels: conversational search tools, generative platforms, and embedded assistants inside apps. These sources may not look like classic referral traffic, but they’re increasingly where high-intent buyers begin their journey.

Tracking this kind of engagement is about recognizing the quality and behavior of the visitors arriving through these new pathways. Segment your analytics to compare how AI-driven referrals perform against traditional organic search: Are they spending more time on site? Are they converting at higher rates? Over time, this will help you identify the true economic value of AI referrals and adjust your strategy accordingly. By aligning KPIs with this new reality, you’ll be better positioned to understand where your most valuable customers are coming from and how to serve them effectively, even as the search landscape continues to evolve.

3. Focus on delivering original, thought leadership content over keyword-driven overviews

The era of casting a wide net with dozens of keyword-targeted posts is fading fast. AI-powered search doesn’t reward sheer volume, it rewards clarity, authority, and depth. Large language models are trained to synthesize content, and when faced with a sprawling collection of surface-level articles, they’ll often bypass them in favor of sources that go deep into a subject. That means your content strategy should shift from trying to capture every possible keyword variation to building comprehensive, authoritative resources that showcase your expertise. A well-researched guide or in-depth explainer will not only perform better with AI-driven search but also resonate more with the high-intent visitors who do land on your site.

This shift also changes the way we think about content planning. Instead of aiming for dozens of quick-turn blog posts, focus on cornerstone content pieces that can serve as definitive resources on key topics relevant to your audience. These pieces can then be supported by complementary assets like case studies, product guides, and customer stories that reinforce the same themes and strengthen your authority in the eyes of both human readers and AI systems. In short, less is more, provided “less” means strategically curated, deeply valuable, and optimized for how modern discovery tools evaluate relevance.

4. Treat customer feedback as the valuable ranking signal it is

LLMs don’t just scan your website—they also draw on a wide range of external inputs, from reviews and testimonials to social media discussions and industry forums. In fact, a recent study shows that Reddit, Quora, and LinkedIn were amongst the most cited websites for Google AI Overviews. 

Every authentic mention of your brand helps reinforce its authority, making it more likely that AI-generated answers will reference you as a trusted source. This makes it critical to foster real, verifiable proof points: customer success stories, ratings on third-party platforms, and a steady cadence of mentions in industry conversations.

This also means that social engagement is no longer just a brand-building exercise, but a direct lever for search visibility. Rather than shying away from platforms where conversations may feel less controllable, marketers should lean into them strategically. Encourage customers to share their experiences, amplify positive feedback, and actively participate in discussions where your expertise adds value. When AI models repeatedly encounter your brand in credible, context-rich settings, they “learn” to trust it.

How to Win in the Age of AI

The decline in organic traffic can feel unsettling, especially for teams that have long relied on SEO as their primary growth driver (that is to say, pretty much every team). But the reality is that search is simply changing, not disappearing. AI may be taking over the top of the funnel, but it’s also filtering out casual visitors and leaving you with the kinds of prospects you’ve always wanted: serious, high-intent buyers.

By shifting your mindset and adapting your strategy, you can turn this challenge into an opportunity. Learn how AI interprets your brand, measure new referral paths, focus on content depth, and listen closely to the voice of your customers. The future of search belongs to those who embrace change, not resist it.

So the next time you open your analytics dashboard and see fewer visitors, don’t panic. Remember: fewer doesn’t mean worse. In fact, in the age of AI-powered search, fewer might just mean better.

When you think of fashion, Artificial Intelligence (AI) probably isn’t the first thing that comes to mind. Nothing about systems that mimic human tasks makes you think of clothing racks. Fashion is all about fabrics and runway models, while AI is more about digits and data models. At first glance, they seem like a very odd couple.

Yet, since the early 2000s, AI has been quietly making its way into the fashion world, starting with tools for data analytics and inventory optimization. In recent years, its presence has become increasingly visible, driving innovations such as virtual try-ons and personalized shopping experiences.

What began behind the scenes is now reshaping the entire industry. Let’s take a closer look at how AI is transforming fashion, one breakthrough at a time!

How AI is Transforming the Fashion Industry

As AI continues to evolve, its role in fashion is becoming harder to ignore. With growing adoption across the industry, AI is actively shaping the way fashion is produced and experienced:

AI in Fashion Design

Nowadays, trends within the fashion industry seem to change every other week, and generative AI is enabling designers to explore more ideas more quickly. Tools like Fashable or Raspberry can turn sketches or mood boards into dozens of generated designs in seconds, helping to accelerate the design process and spark unexpected inspiration. For fashion brands, this means fewer physical prototypes and a better shot at keeping up with ever-evolving fashion trends!

But creativity isn’t the only thing AI brings to the table. With the help of AI algorithms and machine learning, brands can analyze sales history, trend cycles, and customer behavior to guide smarter design decisions, which can lead to collections that better reflect what customers actually want, boosting customer satisfaction and reducing supply chain waste. Whether it’s in-store or online shopping, AI is making the shopping experience more relevant and responsive than ever.

Moncler leads as a good example, as they used generative AI to create the Verone AI Jacket. AI tools helped create quilted textures and concepts for extreme-weather gear, laying the groundwork for both the design and marketing of the collection. It struck the right balance between creativity and precision.

AI in Visual Content Creation

In the visually driven fashion world, content is everything. And AI is transforming how that content gets created. From automated image editing to virtual model generation, AI-driven tools are helping fashion brands produce high-quality visuals faster and at scale. For instance, platforms like FASHN enable rapid fashion content creation through virtual try-on technology, allowing designers and retailers to showcase garments on different models—no traditional photoshoots required. 

These AI tools deliver efficiency as much as they enhance the shopping experience, especially online. By generating polished content at scale, brands can elevate visual consistency and drive higher customer satisfaction. With AI algorithms powering automated editing and virtual try-ons, fashion brands can deliver high-impact visuals while optimizing resources across their content pipelines.

AI in Consumer Experience

One of the most immediate and noticeable impacts AI has on the consumer experience is its ability to personalize both the in-store and online shopping journeys. From smart size suggestions to style matching and helpful chatbots that can answer simple queries, AI helps brands deliver more seamless customer experiences that make shopping easier and more enjoyable.

AI is also reshaping how consumers discover and interact with products. Intelligent systems can analyze browsing behavior and user preferences to suggest items that feel tailored to each individual. For example, Sephora’s Virtual Artist app lets users try on makeup virtually, but the real power lies in its personalized product suggestions. By analyzing skin tone and browsing habits, Sephora’s AI recommends products (like the right shade of foundation or targeted skincare) that shoppers may not have explicitly searched for. It’s as if the algorithm reads their mind before they even know what they want!

AI in Marketing, Data & Forecasting

Marketing in the fashion world has evolved far beyond seasonal campaigns and gut-feel decisions. Today, AI systems and machine learning give brands a data-powered edge, helping them spot emerging fashion trends and optimize campaigns across every channel.

By analyzing real-time data from search behavior and purchasing trends, AI enables more accurate planning and smarter decision-making. Through predictive modeling, brands can identify upcoming trends and even determine the best time to launch new products or campaigns. It helps cut down on overproduction, keeps inventory in check, and ensures marketing hits the right note at the right time, giving brands a much-needed edge in a competitive market!

AI-Powered Search and the Rise of LLMs

As AI continues to evolve, so does the way people search for products. More consumers are turning to large language models (LLMs) like ChatGPT, Google Gemini, and others to ask natural-language questions. This shift signals a growing preference for conversational, AI-powered search experiences that go beyond traditional keyword-based queries.

For fashion brands, this means product data needs to be more than just complete. It needs to be structured, accurate, and easily understood by AI systems. If your product content isn’t accessible or readable by LLMs, you risk being left out of the recommendation loop entirely. By ensuring your product information is enriched and available across the right channels, you position your brand to capture this emerging search traffic and remain visible in a rapidly evolving digital landscape.

AI-Powered Shopping Assistants

AI-powered shopping assistants are redefining how people ask questions and make decisions while shopping online. These assistants, often in the form of intelligent chatbots or voice-activated tools, guide users through the buying journey by answering questions and recommending products in real time.

What makes them so effective is their ability to learn and adapt. These tools use customer data and conversational AI to respond naturally, offering personalized suggestions and mimicking in-store assistance. Take Rufus, Amazon’s AI shopping assistant, for example. It helps users discover products by answering natural-language questions like “What do I need for a beginner ski trip?” or “What’s a good gift for a new parent?”. Rufus provides contextual responses that make product discovery intuitive, enhancing the overall shopping experience.

AI and Sustainability

AI has a reputation for damaging the environment, and for good reason, but when utilized correctly, AI can help to make the fashion industry faster, smarter, and more sustainable in the long run. With the help of intelligent forecasting and supply chain optimization, AI can help brands avoid overstock and reduce waste.

Some companies are even using AI to assess the environmental impact of materials or track the lifecycle of a product. Some AI systems can also help ensure that each product includes the right documentation, like Digital Product Passports (DPPs), and complies with evolving industry standards. By improving transparency and traceability, AI empowers fashion brands to make more responsible choices that align with both their values and consumer expectations.

Integrating AI with PIM

In the fast-moving fashion industry, where product ranges shift rapidly and trends evolve overnight, maintaining high-quality product data is a constant challenge. Akeneo AI-powered PIM helps fashion brands streamline and scale their product information by automatically enriching listings with attributes like color and style directly from product images or descriptions. This automation ensures consistency and helps teams launch collections faster across multiple channels.

A great example of this in action is Courir, a leading fashion retailer that turned to Akeneo Product Cloud to move away from a fragmented, manual process toward a centralized, AI-supported system. By consolidating their product information and automating key workflows, Courir reduced product description and translation time from 10 days to just 24 hours. Manual data entry was cut by 97%, freeing teams to focus on more strategic merchandising. With 96% of their products going live faster and more accurately, Courir not only improved operational efficiency but also elevated the quality of product experiences across every channel.

Where Fashion Meets Intelligence

AI is no longer a future concept, it’s a present-day advantage for fashion brands ready to adapt and innovate. From design and visual content to personalized shopping, AI is reshaping how fashion operates and grows. 

As consumer expectations evolve, integrating AI thoughtfully across the value chain is essential. The brands that embrace this shift will successfully keep up with change as well as lead it.

The eCommerce landscape has expanded rapidly, and it’s showing no signs of slowing down. In 2025, global eCommerce sales are projected to reach $6.83 trillion, and by 2027, online sales are expected to account for an impressive 41% of all retail sales worldwide. Clearly, the future is wide open for brands that embrace eCommerce!

But simply having an online presence is no longer enough. High return rates and inaccurate product content can take away everything you’ve built, damaging both your revenue and your customer relationships. 

Selling online brings scale, but it also brings complexity. That’s why it’s essential to understand not just what’s happening across your digital channels, but why. You need to be able to look back and identify what’s working, what’s not, and what needs to change, before small issues turn into costly setbacks, and that’s where eCommerce performance analytics become critical.

What are eCommerce Performance Analytics?

eCommerce performance analytics refer to the data that’s collected, measured, and interpreted in order to understand how your eCommerce store is really performing. It goes beyond tracking sales or traffic by helping you monitor key performance metrics that influence every stage of the customer journey, from product discovery and the product page experience to checkout and post-purchase interactions.

With the right eCommerce analytics tool, you can identify what’s driving growth and what’s holding you back. Whether it’s a high cart abandonment rate or inconsistent results across marketing channels, tracking the right analytics gives you the visibility needed to take action. By interpreting key data points and listening to customer feedback, you can adjust your eCommerce performance and create a more seamless, engaging customer experience across all your online stores!

eCommerce Performance Analytics That Brands Need to Track

Not all metrics are created equal. While there’s no shortage of data in today’s eCommerce platforms, focusing on the right performance metrics is what separates high-growth brands from overwhelmed ones. 

Here are the key analytics that every eCommerce business should track to gain meaningful insights and improve its eCommerce performance:

1. Conversion Rate

This is the north star for most online stores. Conversion rate tells you what percentage of visitors actually become customers. It reflects how effective your product pages, checkout flow, and overall customer experience really are.
To calculate a conversion rate, use this formula: 

(Total Number of Conversions / Total Number of Interactions) x 100

Small improvements to your conversion rate can really have a big impact on your revenue!

2. Customer Acquisition Cost (CAC)

Knowing how much it costs to acquire each customer helps you assess the efficiency of your marketing channels. Pair it with customer lifetime value (CLV) for a complete picture of whether your acquisition strategy is sustainable—or just expensive.

To calculate a CAC, use this formula:

(Total Cost of Sales and Marketing) / (Number of New Customers Acquired)

3. Customer Lifetime Value (CLV)

This metric estimates the total revenue you’ll generate from a customer over the course of their relationship with your brand. 

To calculate a CLV, use this formula:

(Customer Value) x Average Customer Lifespan

A healthy CLV means strong retention, quality engagement, and a product experience that keeps people coming back. Basically, all the great stuff needed for your business.

4. Average Order Value (AOV)

AOV tells you how much customers typically spend per transaction. Use it to evaluate upselling efforts and how persuasive your product content and pricing strategies really are.
To calculate a CLV, use this formula:

(Total Revenue / Total Number of Orders Placed)

5. Shopping Cart Abandonment Rate

Cart abandonment rate measures the percentage of shoppers who add items to their cart but leave the site before completing the purchase. A high rate signals problems in the final steps of the customer journey—whether due to surprise fees, slow load times, or even a lack of payment options. Fixing this can unlock revenue that’s already sitting in your cart.

To calculate a cart abandonment rate, use this formula:

 (Number of Completed Purchases / Number of Shopping Carts Created) x 100

6. Bounce Rate

If visitors leave your site after viewing just one page, it’s time to rethink your landing experience! High bounce rates often point to disconnects between your ads and product pages. It can also be a mismatch between what you offer and what your customers actually expect.

To calculate a bounce rate, use this formula:

(Total of Single-page visits / Total visits) x 100

7. Click-Through Rate (CTR)

CTR shows how effective your links, ads, or email campaigns are at driving interest. Whether you’re testing subject lines or optimizing calls to action, this metric keeps your marketing performance honest.

To calculate a CTR, use this formula:

(Total Clicks / Total Impressions) x 100

8. Traffic Sources

Understanding where your visitors are coming from, be it social media, search, or direct, is key to optimizing marketing channels and allocating your budget where it matters most.

Some of the most common traffic sources:

• Organic search – Visitors who find your site via unpaid search engine results (e.g., Google).
• Paid search – Traffic from paid ads on search engines (e.g., Google Ads).
• Social media – Clicks from platforms like Instagram, Facebook, LinkedIn, or TikTok.
• Direct – Users who type your URL directly or click a saved bookmark.
• Referral – Visitors who arrive via links from other websites or blogs.
• Email – Traffic driven by email campaigns or newsletters.
• GenAI – This is a newer traffic source, but delineates when traffic comes from LLMs like ChatGPT or Perplexity

9. Rate of Return & Refunds

These metrics offer a window into product satisfaction and fulfillment quality. High return rates often point to misleading content or post-purchase friction, problems that affect both profit and brand trust.

To calculate a return rate, use this formula:

(Final Value – Initial Value) / Initial Value x 100.

10. Churn Rate

Churn shows how many customers stop buying from you over a given period. When paired with retention efforts and sentiment tracking, it helps brands build a more loyal base and learn how they can improve their services.

To calculate a churn rate, use this formula:

(Total of customers lost / Total of customers at the start of the period) x 100

eCommerce Performance Analytics Tools

Tracking eCommerce performance requires more than a spreadsheet and hope. To truly understand what’s working (and what isn’t) across your eCommerce store, you need the right tools, ones that turn raw data into clear takeaways:

1. Google Analytics (GA4)

A staple for nearly every online store! Google Analytics offers deep insight into user behavior, traffic sources, bounce rate, conversion rate, and more. GA4 also brings in enhanced event tracking, making it easier to monitor key actions like cart adds and product views.

2. Shopify Analytics

For brands using Shopify, the built-in analytics dashboard provides a wealth of performance metrics. This includes AOV, cart abandonment rate, top products, and customer segmentation. It’s especially useful for tracking store sessions by device and marketing channels.

3. Hotjar

Hotjar adds a layer of behavioral data through heatmaps, session recordings, and its feedback and surveys. It helps you visualize how customers interact with your product pages and where pain points may be affecting the customer experience—especially when unfinished transactions are high.

4. Adobe Analytics

A more advanced enterprise-level tool, Adobe Analytics allows for deep segmentation, attribution modeling, and predictive analysis. It’s powerful for businesses with complex data needs and large-scale eCommerce platforms looking to scale with precision.

5. Mixpanel

Mixpanel focuses on user behavior over time, ideal for tracking CLV and product usage. It’s especially helpful for businesses offering subscriptions or multi-step customer journeys where engagement is key.

6. Glew.io

Tailored specifically for eCommerce businesses, Glew.io brings together sales, product, customer, and marketing data into a unified dashboard. It’s great for identifying high-performing SKUs and analyzing acquisition costs by channel.

How Akeneo Business Analytics Helps

While clean product data is essential, understanding how that data impacts your eCommerce performance is where real value is unlocked. Akeneo Business Analytics, part of the Akeneo Product Cloud, gives brands visibility into how product content quality drives results across their eCommerce platforms.

With a centralized dashboard, teams can monitor key performance metrics like total page views, conversion rate, revenue (and more) all across up to 10 digital and physical sales channels. This unified view replaces fragmented data silos and helps brands understand how product content is performing in both their online and physical retail channels. Whether your strength lies in eCommerce or in-store selling, you’ll have the insights needed to optimize the customer journey and build a more data-driven growth strategy!

Analyse Harder, Perform Better

As eCommerce performance becomes a defining factor in retail success, the ability to measure and act on data is a competitive necessity. Whether it’s optimizing a product page or lowering your cart abandonment rate, the right analytics turn insights into impact.

But performance doesn’t start with analytics—it starts with high-quality data. With solutions like Akeneo Product Information Management (PIM), brands can ensure their product information is not only accurate and consistent but also ready to drive smarter decisions across every stage of the customer journey. Because when your data works harder, your eCommerce business performs better.

Just a few years ago, generative AI was more novelty than necessity. It was a fascinating experiment that made us stop and say, “Wait, a computer wrote that?” 

I remember testing out ChatGPT when it first launched and being amazed that it could string together paragraphs that not only made sense but actually sounded human.

Fast forward to today, and GenAI has moved well beyond a fun curiosity. It’s woven into everyday decision-making, helping people plan vacations, decide what’s for dinner, and, increasingly, discover and choose the products they want to buy.

According to a recent Prosper Insights & Analytics survey, nearly one-third of U.S. adults already use AI tools to assist with everyday decisions, and Adobe also reported a 1,200% spike in AI-driven referral traffic in just eight months, showing just how quickly AI is shifting digital behaviors.

And it’s not just consumers. B2B buyers are adopting GenAI-powered search at an incredible pace. G2 recently announced that 8 in 10 business decision-makers believe AI search has already changed how they conduct research, and almost one-third say they now start their search on platforms like ChatGPT more often than Google.

The way people find information, products, and brands is fundamentally changing, and businesses need to adapt and grow in order to keep up.

The Impact of GenAI Search on SEO

For decades, SEO (Search Engine Optimization) has been the backbone of digital marketing and product search, with ranking high on Google synonymous with being discoverable. But GenAI is rewriting the rules of the game. 

Instead of scanning a list of blue links, people now receive AI-generated answers that summarize content from across the web. Google’s AI Overviews, ChatGPT responses, and other GenAI-driven platforms often present concise, synthesized answers, which means users don’t always click through to websites.

We’re already starting to see the impact of these AI-powered search experiences. Some industries are reporting organic declines of 15 to 64 percent as a direct result of Google’s AI Overviews. Meanwhile, nearly 80 percent of search users admit they rely on AI-generated summaries, often turning to them for quick answers rather than digging deeper into websites. Even on traditional search engines, almost 60 percent of queries now end without a single click because the answer is provided upfront without needing to click a link. 

Taken together, these shifts show that while SEO still matters, it’s no longer enough on its own. To stay visible in this new landscape, businesses need to expand their strategy to include what’s called GenAI Engine Optimization (GEO).

What is GenAI Engine Optimization (GEO)?

If SEO is about optimizing your content to rank higher on search engines like Google, GEO is about making your brand and products discoverable within generative AI platforms.

Large language models (LLMs) like ChatGPT, Claude, and Gemini aren’t just crawling keywords; they’re synthesizing knowledge from a wide range of sources. They’re looking for clear, structured, authoritative information that they can confidently surface in their generated answers.

Put simply: GEO is the practice of shaping your product information, content, and digital presence so that GenAI engines can easily find, understand, and include your brand in their outputs.

How to Optimize for GEO

Optimizing for genAI isn’t about throwing out everything you know from SEO, but about evolving your approach to meet the way people (and machines) now search. Traditional search engines rewarded keyword alignment and link-building strategies, but large language models like ChatGPT, Gemini, and Perplexity operate differently. They prioritize structured, reliable, and authoritative data they can confidently summarize and recommend to users. So let’s take a look at a few ways businesses can start optimizing their data for genAI engines.

1. Understand where and how you already appear in LLMs

To begin optimizing your presence in generative AI platforms, you first need to understand your starting point – how AI actually “sees” your brand and products. A simple way to do this is by running test queries in tools like ChatGPT, Gemini, or Perplexity: Are your products showing up? Are they described accurately? 

This baseline check often reveals gaps, such as incomplete data, outdated details, or even total invisibility. This is where a solution like Akeneo’s AI Discovery Optimization can become incredibly helpful as it allows you evaluate precisely how your products appear in AI-driven search experiences.

With these insights, businesses can move beyond guesswork and better understand not only whether your products surface but also why, pinpointing missing attributes, inconsistent formatting, or content that doesn’t match how customers search in natural language. Armed with clear, actionable recommendations, you can enrich product information strategically, improve visibility across platforms like ChatGPT and Perplexity, and ensure your products are represented accurately and persuasively in the fast-growing world of generative AI search.

2. Focus on creating structured, organized, and well-labeled product content

AI thrives on structure, and the clearer and more consistent your product data is, the more likely it is to be understood and referenced by large language models. Standardizing attributes such as product titles, descriptions, categories, and metadata helps create a consistent framework that AI can easily interpret. 

Equally important is labeling data clearly through schema markup and structured product information, which gives AI stronger signals about meaning and context. Eliminating ambiguity is also key, as vague or incomplete descriptions can easily be misinterpreted by generative AI systems, and lead to poor visibility or inaccurate representations of your products.

This is where Product Information Management (PIM) systems like Akeneo can play a vital role. By centralizing product data in one place, ensuring accuracy, and maintaining consistent formatting across channels, PIM makes your content both customer-friendly and AI-ready. A well-structured data foundation not only improves the way your products are presented to human audiences but also enhances their chances of being accurately referenced and recommended by AI-powered search and discovery tools.

3. Create a system for data consistency and clarity

Inconsistent or unclear data doesn’t just confuse customers, it also undermines how generative AI tools understand and present your products. If the same item looks different on your website, in a marketplace, and on a partner channel, AI may struggle to connect the dots, which can weaken your presence in search results and reduce discoverability.

That’s why consistency and clarity matter. Product attributes should remain uniform across all platforms, using plain and descriptive language that AI can easily parse. For companies operating internationally, ensuring multilingual consistency is equally important to prevent misinterpretation across regions. At the end of the day, AI tools are only as strong as the data they’re trained on, so the clearer and more reliable your product information, the stronger your visibility will be in GenAI-driven discovery.

4. Publish authoritative, original content

GenAI engines are designed to pull information from trusted, authoritative sources, which means that if you want your brand to appear in AI-generated answers, you need to establish yourself as one of those sources. Building authority starts with publishing original, high-quality content that demonstrates your expertise. Thought leadership pieces such as articles, whitepapers, and research can position your brand as a go-to resource within your industry, while product-focused content like guides, FAQs, and explainers ensures that customers—and AI tools—have clear, accurate information to draw from.

Credibility also plays a critical role. Incorporating citations, references, and expert commentary signals to both human readers and AI systems that your content can be trusted. The stronger and more authoritative your content, the more likely it is to be surfaced by generative AI engines when users are searching for answers.

The Future of Search and Discovery

Generative AI has already reshaped the digital landscape, turning search into a conversation and discovery into an AI-driven experience. For businesses, this shift presents both a challenge and an opportunity: the challenge of declining organic traffic through traditional channels, and the opportunity to stand out in the new environments where buyers are making decisions.

GEO provides the bridge between yesterday’s SEO playbook and today’s AI-powered reality. By investing in structured product data, ensuring consistency and clarity across channels, and publishing authoritative, trustworthy content, businesses can position themselves not just to survive this shift, but to thrive in it. 

Search has always been about connecting people with the right information at the right time. What’s different now is the medium. The organizations that embrace GEO will not only remain discoverable in this new era, they’ll build stronger, more trusted connections with customers who are increasingly guided by AI.

In a relationship, any therapist will tell you that clear communication is the foundation for success. Both people need to understand each other fully, or misinterpretations and frustration quickly follow. 

The same holds true for a relationship between a customer and a business; if the customer doesn’t receive clear, consistent, and accurate information, trust begins to erode and the relationship starts to break down.

The unfortunate reality is, this miscommunication and misalignment happens all too often. In the past year alone, two-thirds of consumers abandoned a significant purchase because product information was missing or inaccurate. That’s a direct hit to revenue, trust, and retention. Customers can’t make confident decisions when the details they need aren’t there, and in today’s competitive market, they won’t hesitate to move on to a competitor who delivers the clarity they expect.

But how exactly does poor product data damage sales, and what exactly does ‘poor product data’ even look like in the market? Let’s break down the biggest ways poor content holds brands back and how you can avoid these costly pitfalls!

What Does Poor Product Data Look Like?

Before we can fix bad data, we need to know how to spot it. Poor product information isn’t always obvious—it doesn’t just mean missing details. It shows up in many ways across the customer journey, each with the power to lessen a customer’s commitment to a product or even worse, a brand.

It might look like incomplete specs that leave shoppers guessing about size, fit, or compatibility. Or inconsistent pricing between your website and marketplace listings that makes buyers suspicious. It can even be vague or generic descriptions that make your product sound like everyone else’s, or worse, conflicting details across channels that create confusion instead of clarity!

How Poor Product Data Impacts Your Business 

As we all know, today’s shoppers expect clarity, relevance, and transparency, and when businesses fall short, the consequences are immediate and costly. From abandoned carts and missed conversions to rising return rates and eroded loyalty, poor product data doesn’t just create frustration; it directly translates into lost sales and weakened customer trust.

1. Rising Dissatisfaction Hurts Conversions

Customer patience with poor product information is wearing thin. In 2023, only 13% of consumers said they were dissatisfied with the comprehensiveness of product data, but by 2025, that figure jumped to 30%, more than doubling in just two years. As frustration rises, shoppers won’t hesitate to abandon their carts, turning poor product data directly into lost sales.

2. When Details Are Missing, Purchases Disappear

As we mentioned earlier, a lack of product information led two-thirds of shoppers to give up on a major purchase within the past year. And the damage doesn’t just stop there. Globally, we found that 77% say they’d even consider switching to a lower-cost, lower-quality alternative if the right details weren’t available. Essentially, your poor product data can result in your cheapest competitor making a sale!

3. Returns Reveal the High Price of Inaccurate Data

Two-fifths of consumers globally returned a product in the past year because the details didn’t match the reality. Returns like these drain your bottom line and your credibility, leaving customers cautious instead of confident.

4. Generic Content Pushes Shoppers Away

One-size-fits-all descriptions don’t persuade modern shoppers. Customers expect details that speak to their needs, values, and preferences. Without relevance, they disengage and abandon businesses. It’s no surprise that over half of consumers say they would become more loyal to brands that provide personalized shopping experiences. In today’s world, generic content fails to keep shoppers’ attention, costing long-term relationships.

5. Lack of Transparency Leaves Money on the Table

Today’s consumers want brands to reflect their principles. Yet brand values, sustainability claims, and supply chain transparency are consistently rated among the least comprehensive areas of product information. The cost of this omission is high: 42% of consumers say they would pay more if a brand clearly shared its values, with those willing to do so prepared to spend an average of 25% extra. Without a doubt, customers will spend less and look elsewhere for a brand they can believe in.

What Does Good Product Data Look Like

So, we know what bad product data looks like, and how it can impact your business. But now, we need to take a look at what good product data looks like – and how you can provide it to customers.

1. Accurate and Consistent

Make sure every detail, from pricing to specs to compatibility to availability, matches across all channels. Customers see the same story whether they’re on your site, browsing a marketplace, or shopping in-store. Utilizing a syndication tool such as Akeneo Activation can allow you to automatically send enriched, accurate, and reliable product information to every channel and marketplace, ensuring consistency and a unified product experience wherever your shoppers engage!

2. Completeness That Builds Confidence

Good product data answers every key question before it’s asked. Size, dimensions, technical features, and compatibility details are all covered, leaving no room for hesitation!

3. Enrichment With Context

Beyond the basics, strong product content includes sustainability credentials, allergen or nutritional details, brand values, and more. These enrichments should meet the rising demand for context and authenticity.

4. Personalization and Relevance

Great product data adapts to the customer! Tailored recommendations, personalized messaging, and content that reflects prior behaviors all turn information into a driver of loyalty. But in order to provide such a tailored experience, you need to understand what your customers want, and how they actually speak about and use your products. This is where a tool like Akeneo’s PX Insights can come in handy. By pulling in real-time insights from real customer feedback directly into your product information management solution, you’ll be able to personalize your product data to match customer language and expectations.

5. Visual and Multimedia Support

High-quality images, videos, and other types of media help customers understand products better than words ever could. And the future of product storytelling goes even further. Virtual reality (VR) and augmented reality (AR) tools allow shoppers to interact with products in immersive ways, seeing how furniture looks in their living room or visualizing clothing fit in 3D. Rich visuals close the gap between physical and digital experiences.

How PIM Can Help

Now that we have a proper understanding of what sets good product content apart from bad product content, the question becomes, how do you provide high-quality product data to customers? 

The truth is, many businesses struggle to provide consistent, reliable product data to consumers because their product information is scattered across spreadsheets, legacy systems, or siloed teams, which makes errors inevitable. Without a centralized way to manage content, those gaps only widen, causing even more damage to your business.

This is where a Product Information Management (PIM) solution changes the game. By creating a single source of truth, a PIM ensures every detail is accurate, complete, and consistent, no matter where customers encounter it. Beyond solving errors, PIM scales your content across channels and integrates with enrichment technologies like AI to tailor and enhance experiences for individual shoppers. Pair that with personalization and the result is clear: higher conversions, fewer returns, and stronger customer loyalty in an increasingly competitive market!

Turning Product Data Into Growth

Poor data creates friction, and in today’s competitive market, where customers are less forgiving, they won’t wait around for you to fix it. They’ll turn to a competitor who delivers the clarity they need.

The good news is, businesses don’t have to settle for disjointed, error-prone content. With the right strategy and the right tools, you can transform scattered, inconsistent data into a powerful driver of conversion, satisfaction, and long-term growth. Put simply: clear communication builds trust in personal relationships, and it does the very same in commerce. If you want to win customers—and keep them—start with better product data.

 Want to dive deeper into how consumer expectations are evolving? Download our latest Consumer Survey Report to discover the full findings and uncover what today’s shoppers really need from your product information.

In today’s world, information is everywhere, but consistency is rare. As organizations expand across channels, regions, and customer touchpoints, the cracks in disconnected or poorly managed data systems become harder to ignore.

That’s why many businesses are turning to MDM and, more importantly, a clear MDM strategy to bring order to the chaos. Without a strategic approach, even the most advanced tech stack can result in conflicting product information or missed opportunities for personalization and efficiency.

So, what does a successful MDM strategy actually look like? Let’s dive in so you can get a better idea of what it is, why it matters, and how you can implement it within your own business!

What Is Master Data Management?

More than just a technology solution, Master Data Management (MDM) is a holistic approach that creates and manages a single, consistent, and accurate source of master data across an organization, ensuring accuracy and uniformity. It’s the framework that ensures your organization’s core data, including customer, product, supplier, and other information, is accurate and accessible to all who need it.

By centralizing and standardizing master data, MDM helps create a unified view that supports everything from day-to-day business processes to long-term strategy. The goal is to eliminate data silos and provide a single source of truth that teams across departments, like marketing, sales, and operations, can rely on to work with the same set of accurate data.

What Is a Master Data Management Strategy?

Essentially, an MDM strategy is the blueprint for how your organization implements and governs MDM over time. It’s a structured, long-term plan that defines how you’ll collect, maintain, and scale your master data across systems and stakeholders, ensuring alignment between people, processes, and technology. 

A strong MDM strategy defines everything from the role of data stewards and data governance policies to how MDM integrates with your broader business processes, tech stack, and operational goals.

Key Aspects of a Master Data Management Strategy

Whether you’re just getting started or rolling out enterprise-wide MDM programs, your strategy is the foundation that keeps your data initiatives focused, scalable, and future-ready.

Here are the key ingredients that turn MDM from simply a word into a competitive advantage:

1. Data Governance

At the heart of any MDM strategy is a strong data governance framework. This includes the rules, policies, and standards that define how data is created, maintained, accessed, and retired across the organization. Governance provides clarity around who owns which data domains and ensures all departments are aligned on how to manage that data.

It’s not just about structure, it’s about your sanity. With the right policies in place, it reduces the risk of data silos and duplication, supports regulatory compliance, and ensures your master data remains a trusted foundation for operations and decision-making. Without it, even the best MDM solutions are at risk of becoming disorganized and underutilized.

2. Data Stewardship

Data stewards play a crucial role in translating your MDM strategy into day-to-day results. They’re responsible for upholding data quality standards, resolving discrepancies, and applying the governance rules to ensure every piece of master data meets your organization’s standards. Think of them as the quality control team for your information infrastructure!

Stewards also serve as the bridge between business and IT teams, translating techy rules into real-world workflows. They make sure the data used in analytics and operations is not only accurate but also relevant and accessible, and their involvement helps drive accountability and fosters a culture of data ownership, critical for the long-term success of any brand.

3. Data Quality

No strategy can succeed without a strong focus on data quality. Complete and consistent data ensures that your operations run smoothly, your analytics are trustworthy, and your customers have accurate and up-to-date information at every touchpoint. Without it, you’re essentially flying blind—or worse, making decisions based on flawed assumptions.

An MDM strategy should include tools and processes that actively monitor and improve data quality over time. This can include validation rules and duplicate detection. When done right, you get accurate analytics, smoother operations, and fewer customer support emails that start with “this isn’t what I ordered.” Everyone wins!

4. Data Integration

Your MDM implementation doesn’t get to live on an island. It needs to shake hands with your ERP, CRM, PIM, and any other acronym-heavy platforms you rely on to keep the business running! If your systems don’t talk to each other, your data won’t either. This ensures that accurate data flows consistently across the organization. Without strong integration, data fragmentation persists, and your MDM efforts risk becoming disconnected from the tools your teams rely on regularly.

A fully integrated MDM approach helps maintain a unified view of your business, where product, customer, and supplier data stay consistent, no matter where they’re being used. 

5. Data Security

As data volumes grow, so do the risks. A robust MDM strategy must include clearly defined security policies to protect sensitive data, particularly customer information, from breaches, misuse, or non-compliance. This includes setting access controls, encryption protocols, and role-based permissions that limit exposure and ensure data integrity.

In addition to protecting data from external threats, your security measures should also support compliance with data privacy regulations like GDPR or CCPA. A secure MDM strategy gives your teams the freedom to work confidently and your customers peace of mind that their data isn’t being passed around.

Challenges of Implementing an MDM Strategy

Even the best MDM strategy can run into roadblocks on the way to becoming a reality. Here are some of the most common (and frustrating) challenges organizations face when trying to get their master data management efforts off the ground:

1. Internal Silos and Misaligned Teams

It’s hard to manage data as “one version of the truth” when every department is working off its own. Without alignment across teams, MDM can be challenging if priorities, data definitions, or ownership responsibilities are unclear or inconsistent.

2. Legacy Systems and Integration Challenges

Many organizations rely on outdated or rigid legacy systems that weren’t built with modern data integration in mind. Connecting these systems to a new MDM solution can require significant customization, which increases cost and complexity.

3. Pre-Existing Data Quality Issues

Introducing an MDM strategy doesn’t instantly resolve poor data quality. Many organizations begin with master data that is already inconsistent or duplicated, issues that must be addressed early on to avoid carrying old problems into a new system.

4. Ongoing Governance and Maintenance Requirements

MDM is not a one-time project, it’s an ongoing commitment. Effective data governance and data stewardship must be continuously maintained to adapt to changing business processes and new data sources.

MDM and PIM

While Master Data Management provides a centralized approach to managing an organization’s core data, Product Information Management (PIM) focuses specifically on product data: descriptions, attributes, images, translations, and channel-specific content. PIM systems are purpose-built to enrich and distribute product information across platforms such as eCommerce.

Together, MDM and PIM form a powerful combination. MDM creates consistency and control over foundational data across systems, while PIM delivers the flexibility and depth needed to manage complex, customer-facing product content. When integrated, they help ensure high-quality, accurate product experiences while aligning with your broader data management strategy!

Laying the Foundation for Long-Term Data Success

A successful Master Data Management strategy is more than a technical initiative, it’s a business-critical investment in accuracy and agility. From improving data quality and supporting governance to enabling better decision-making and scalable growth, MDM plays a foundational role in how organizations manage their most valuable data assets.

While the journey can involve challenges, establishing the right strategy, supported by the right tools and people, sets your business up for long-term success! And when paired with a focused solution like PIM, MDM becomes even more powerful, turning consistent data into a true competitive advantage.

Here’s a question that is crucial to the success of any business, but can be incredibly difficult to answer:

What matters most to today’s shoppers?

It may sound like a simple question, but in reality, the path to purchase has become more complex than ever.

Shoppers move fluidly between digital and physical channels, engaging with an average of six touchpoints before making a purchase. In fact, 73% of consumers use multiple channels throughout their shopping journey. This modern shopper is not only more informed but also more values-driven, weighing factors such as brand ethics, sustainability, and authenticity alongside price and product specs.

And of course, adding to this complexity is the growing, and sometimes contradictory, role of AI in shaping the customer experience. Many consumers express hesitation and even skepticism about how companies deploy AI, worrying about misuse, privacy, or impersonal automation. Yet, at the same time, they increasingly expect deeply personalized, intuitive, and seamless experiences; demands that are, in large part, powered by AI. 

With all of that in play and more, it’s no wonder that a simple six-word question becomes convoluted and nuanced. That’s why we set out to find real answers by surveying 1,800 consumers across eight countries (the United States, United Kingdom, Germany, France, Netherlands, Sweden, Australia, and Italy) to get a clearer view of what today’s shoppers value, what turns them off, and how businesses can better meet their expectations. 

You can check out the full report of our findings here, but let’s take a quick look at some of the key insights into the modern shopper that we uncovered.

7 Key Insights Into the Modern Shopping Journey

1. Inaccurate Product Information Hurts Sales

Shoppers expect up-to-date, accurate product information, and they won’t hesitate to walk away when it’s missing. In our survey, we found that two-thirds of consumers worldwide were reported to have done this in the last year, showing that weak product information directly fuels lost trust and brand switching.

The good news is that it’s not all doom and gloom; we also found that almost half of all consumers say they would pay more if retailers offered complete, high-quality product information—on average, about 25% more per product. For brands, the answer is simple: investing in richer, more reliable product data, fueled by Product Information Management (PIM), leads to both increased revenue and stronger customer relationships.

2. Personalized Shopping = Stronger Customer Bonds

A tailored shopping experience streamlines the process and encourages higher spending. Two-fifths of consumers say they would pay more for personalization, on average, about a quarter more when interactions feel relevant. For brands, building these touchpoints is a proven way to increase value with every purchase.

The benefits don’t stop at revenue. Over half of consumers say they would become more loyal to a brand or retailer that offers a personalized experience, proving that relevance builds stronger relationships as well as bigger baskets. In a crowded marketplace, meaningful experiences set brands apart, turning one-time visitors into repeat customers and long-term advocates.

3. AI Tools Are Guiding Shoppers to the Right Choice 

AI-powered shopping tools, whether virtual assistants, virtual reality, or chatbots, are becoming trusted guides in the buying journey. By quickly surfacing relevant specs and features, they remove friction and give customers the confidence to move forward with a purchase.

In markets like France, where 40–45% of shoppers are interested in AI-driven tools such as AI agents or voice assistants that can list features and answer questions, the potential is clear. By simplifying complex choices, these tools empower shoppers to make decisions quickly and confidently.

Read about Akeneo’s most recent AI capabilities in our latest summer release. 

4. Free Returns Are the New Expectation

Incorrect or incomplete product information does more than just cost a sale. The lack of the two can bring a product right back to your warehouse. In the past year, two-fifths of consumers have sent their items back because the pre-purchase information didn’t match reality. That’s a clear sign that terrible content has terrible consequences long after checkout.

And shoppers aren’t forgiving when it comes to return policies. Two-thirds feel negatively if a retailer charges them for returns, while only a small fraction are understanding. The takeaway here? Getting product content right the first time is critical to avoid operational headaches as well as protect your margins and preserve your customers’ confidence.

5. Sustainable Values, Sustainable Revenue

Today’s shoppers want their purchases to reflect their values, and they look for transparency as reassurance. Yet brand values like sustainability or regulations compliance, as well as nutritional information, supply chain practices, and even influencer testimonials, are still rated as some of the least comprehensive parts of product content.

That gap presents a real opportunity. 42% of consumers say they would pay more if brands clearly shared their values as part of product information, and those who would are prepared to spend an average of 24% more. In fact, over a third would even pay more than 10% extra because transparent commerce is enough to justify a higher price. More than an ethical stance, brand values are a requirement for expanding your customer base.

6. How Influential Voices Steer Shoppers’ Choices

User reviews stand out as one of the most powerful forces guiding purchase decisions. Globally, two-thirds of consumers have bought a product based on comments or feedback from other shoppers, making reviews even more influential than expert or influencer endorsements. Authentic content builds confidence in a way polished product pages alone can’t.

We also found that influencers still play an important role, with more than half of consumers saying they’ve made a purchase based on their recommendations, especially in categories like beauty, skincare, supplements, and sports equipment. But when it comes to credibility across categories, reviews often carry more weight. In France, for example, 67% of shoppers say user comments have swayed their purchases.

This influence extends beyond impulse buys. Nearly half of global consumers say they would be more likely to purchase decorative items, cultural products, sports gear, or luxury goods if they saw candid reviews or demonstrations from similar shoppers. The message is clear: social proof, especially those like user reviews, remains one of the strongest drivers of buying behavior worldwide.

7. In-Store and Online Work Hand in Hand for Today’s Buyer

Today’s shoppers don’t rely on a single channel when making purchases. In fact, general and specialty retail stores (30%) and online marketplaces (27%) rank as the most common shopping destinations, while for product discovery, consumers lean heavily on traditional search engines (26%) and marketplaces (22%). Bouncing between these channels highlights how important it is for brands to deliver accurate, consistent product information across every touchpoint, whether digital or physical.

But availability alone isn’t enough—experience matters just as much. Shoppers expect free delivery (38%), free returns (33%), and an easy return process (28%) as part of the standard retail package. Inconsistent product information and poor service drive customers away, while consistency and flexibility keep them coming back.

Turning Insights Into Action

Today’s B2C shoppers demand accuracy, personalization, and seamless experiences across every channel. They expect brands to deliver reliable product information, reflect authentic values, and connect with them through relevant, meaningful content. Miss these expectations, and you risk weakening one of the strongest foundations for growth—long-term trust.

The findings from our B2C survey show that accurate product information is key to growth. Brands that treat it as a strategic asset and maintain consistency across touchpoints won’t just win the sale, they’ll win loyalty, advocacy, and a competitive edge in a crowded market.

For a deeper dive into the trends and statistics shaping the global B2C landscape, download our latest 2025 B2C Consumer Survey Report and learn how to place your brand ahead at the moments that define customer decisions.

If there were one word that perfectly captures the essence of product detail pages (PDP), it would be transparency. These pages lay it all out, offering a clear and comprehensive view of what a brand is really offering. Just by scrolling through one, you’re immediately presented with an intricate mix of information, the kind you want to know as a curious shopper and the kind you need to know to make a confident purchase.

However, there’s far more happening behind the scenes than its simple appearance suggests. For a page to look seamless and engaging on a customer’s screen, it takes careful planning, thoughtful design, and ongoing maintenance. Let’s see what makes an effective PDP!

What is a Product Detail Page (PDP)?

At its core, a product detail page is designed to inform. It provides essential facts like product descriptions, pricing, specifications, images, and availability, and should include customer reviews, size and color options, shipping costs, and an easy‑to‑spot add to cart button. Basically, all the essentials that customers need to make informed purchase decisions! 

In practice, a PDP does so much more. It answers questions. It tells stories. It builds trust. And ultimately, it gives customers the information they need to hit “Add to Cart” with confidence.

A well‑designed product detail page is where interest turns into action. This is often the page where shoppers decide whether to proceed with a purchase, making it a critical driver of sales and customer confidence. By consolidating everything from product specifications, availability information, reviews, and other supporting content, a PDP equips customers with reassurance and confidence.

Beyond influencing individual buying decisions, PDPs also play a strategic role in overall site performance. They enhance trust in a brand, reduce friction in the shopping process, and support long‑term customer loyalty by delivering an experience that feels both reliable and engaging. In short, a strong PDP doesn’t just sell a product, but instead reinforces your brand promise and improves the entire customer experience.

What Makes Up a Great Product Detail Page?

You’ve probably gathered by now that a product detail page actively shapes the customer journey, not only introducing shoppers to a product but also building the confidence they need to make a purchase. 

And a cohesive appearance surely helps to make all of that happen! However, while a PDP may look polished on the surface, the impact of it comes from different elements of the page working together with a purpose:

• Menu & search bar: Easy access to navigation and search tools helps shoppers explore your site beyond a single product.
• Breadcrumbs navigation: No, I don’t mean actual food. Breadcrumbs are a navigational aid located at the top of a page. It shows users (and search engines) the path to the current page, helping customers understand where they are on the site and easily navigate back to previous pages.”
• Product title: A concise, descriptive title that immediately tells shoppers exactly what they’re viewing!
• Detailed product descriptions: Go beyond the basics to highlight features and benefits in a way that resonates with your audience.
• High‑quality images: Include images of the product with multiple angles, zoom options, and even lifestyle imagery or videos that bring the specific product to life. However, some platforms, such as Amazon, require images to look a certain way. Make sure your photos follow the requirements of the platform it’s being sold on!
• Customer reviews & ratings: Social proof helps build trust, addresses hesitations, and improves conversion rates. It’s also a way of advertising how global your audience is.
  Pricing, shipping & return details: Provide transparent shipping costs, shipping options, return policies, and availability information to reduce surprises at checkout.
• Variation and customization: Options for size, color, and other attributes should be easy to find and select.
• Compelling calls‑to‑action: A clear, prominent buy/add to cart button keeps the next step obvious and accessible. It guides customers seamlessly from learning to buying. Make sure your CTAs stand out and grab attention instantly. 
• Policies: Make your shipping, return, and warranty policies visible and easy to understand to build customer trust and minimize pain points.

When these components come together, a PDP transforms from a static page into a powerful tool that both informs and converts!

Walmart Leading By Example

A great example of a well‑designed product detail page can be seen with one of our own partners, Walmart. As you can see on the PDP for a children’s backpack, it features several key elements of an effective product detail page, including a clear title, pricing, an add‑to‑cart button, a detailed product description, and more!

The Right Technology to Power Enhanced Product Detail Pages 

A well‑crafted PDP is only as good as the data behind it. And that’s where Akeneo Product Cloud comes in. By centralizing and enriching product information, Akeneo PIM (Product Information Management) ensures every PDP displays consistent and accurate content across all eCommerce sites and channels. From detailed product descriptions and standout features to images, videos, specs, and care instructions, PIM makes it easy for brands and retailers to deliver rich, engaging product experiences. This level of detail not only enhances customer trust and satisfaction but also plays a key role in reducing returns and boosting conversion rates.

But creating enriched product information is only half the battle—you also need to get that content where it matters most, which is where a solution like Akeneo Activation comes in.

Akeneo Activation helps brands seamlessly syndicate their product information to top retail and marketplace channels like Amazon, Walmart, Target, Zalando, and more. Whether you’re selling across your own D2C site or third-party marketplaces, Akeneo Activation ensures your product content is tailored to meet each channel’s unique format, requirements, and audience expectations.

No more manually reformatting product data or worrying about inconsistent messaging across your channels. With Akeneo Activation, enriched PDP content flows smoothly from your PIM into the hands of your shoppers.

The Product Detail Page Drives the Product Experience

A product detail page is ultimately a tool that guides shoppers, answers their questions, and helps them feel confident about buying. When elements like detailed product descriptions, customer reviews, clear features and benefits, transparent shipping costs, easy‑to‑find return policies, and a standout add‑to‑cart button all come together, a PDP transforms into a trust‑builder that drives sales and boosts conversion rates.

And with the right tools, like Akeneo PIM, creating and maintaining these pages doesn’t have to be overwhelming! By centralizing and enriching product data, you can ensure every PDP on your eCommerce site is consistent, accurate, and engaging — whether viewed on desktop or mobile devices. In doing so, you don’t just enhance individual specific product pages; you elevate the entire online shopping experience.

Have you ever tried cooking with a poorly written recipe? You know the kind – vague measurements, missing steps, ingredients listed out of order. You spend more time second-guessing than actually cooking, and the end result rarely turns out the way it should.

Working with artificial intelligence (AI) on top of messy product data feels a lot like that.

AI has incredible potential to transform how businesses manage and scale product information. It can generate product descriptions, power intelligent search, personalize recommendations, and help you go to market faster across every channel. But just like a recipe, it needs clear, complete, and reliable instructions—your product data.

When your product data is inconsistent, incomplete, or scattered across systems, AI can’t perform at its best. In fact, it may even cause more problems than it solves. That’s why the first step in any AI journey should be getting your product data in order.

Why Focus On Product Data First?

Artificial intelligence isn’t magic. It’s pattern recognition at scale. Whether you’re tapping into generative AI to write compelling product descriptions or using predictive models to suggest upsells, all AI systems depend on one critical ingredient: data. And not just any data – clean, structured, and consistent product data.

AI thrives on well-organized inputs. It needs reliable patterns and clear relationships between data points to draw insights or make predictions. When you feed it high-quality product information, it can identify trends, fill gaps, and even anticipate customer needs. But when that data is messy or incomplete? Things fall apart.

Let’s say your AI is tasked with generating SEO-friendly product titles. If it pulls from inconsistent naming conventions where one item is called a “crewneck pullover” and another a “long-sleeve fleece” for nearly identical products, it won’t know which terminology to standardize or prioritize. Or imagine a recommendation engine working off missing sizing information; it may suggest irrelevant or ill-fitting products to shoppers, causing frustration and returns.

That’s why product data should come first, before you automate, optimize, or personalize.

Think of it like building a smart home. You wouldn’t start wiring your house for voice-activated lighting or automated blinds before making sure the floors are level and the plumbing works. Without a solid foundation, all that smart functionality is compromised. 

It’s the same with AI: get the basics of product data right, and automation, personalization, efficiency becomes far more effective and reliable.

Good AI doesn’t replace good data hygiene – it builds on it.

Risks of Implementing AI with Messy Data

Companies excited to deploy AI without cleaning up their data often encounter unexpected consequences, including:

• Inaccurate or misleading product listings: AI-generated descriptions pulled from poor source data can lead to incorrect claims, like saying a jacket is waterproof when it’s not. That’s a fast track to unhappy customers, negative reviews, and costly returns.

• Faulty recommendations and inaccurate personalization: Product recommendation engines depend on well-structured attributes (size, color, use case, materials). If those fields are missing or incorrect, AI might suggest winter coats to shoppers browsing bikinis.

• Poor search functionality and discoverability: AI-powered search and filtering tools rely on good taxonomy and attribute tagging. If similar products use different terminology (e.g., “blush pink” vs. “light rose”), they may not appear in the same search results.

• Amplification of errors at scale: AI accelerates everything, including mistakes. If your product feed contains incorrect dimensions or pricing, and AI uses that feed to populate 10,000 listings across channels, the error now lives in 10,000 places.
• Regulatory and legal compliance risks: Bad data can lead to non-compliance with product labeling laws, ingredient disclosures, or safety regulations, especially in industries like food, cosmetics, and electronics. AI doesn’t inherently know what’s legal or ethical; it follows your lead. 

What AI-Ready Product Data Looks Like

So what exactly does clean, AI-ready product data look like? There are 6 key traits of AI-ready product data:

1. Structured

Your data needs to follow a clearly defined format with proper categorization and hierarchy. Think of it like a family tree for your products. Parent products should be connected to their variants (colors, sizes, styles), and attributes should be broken down into specific fields, like material, size, dimensions, or use case. Without structure, AI can’t navigate your data or draw reliable conclusions.

2. Complete

AI can’t work with what it can’t see. Incomplete data like missing product titles, specs, or images leads to poor outputs. Ensure that every product listing contains all the necessary fields and content across every category. Completeness is a prerequisite for AI performance.

3. Consistent

Standardization is crucial. If one product lists its color as “navy” and another as “midnight blue,” AI might treat them as unrelated even if they’re the same item in different channels. Consistency allows AI to recognize patterns across your product catalog and make smart associations.

4. Enriched

Basic specs alone won’t cut it. AI needs context to do its best work, whether that’s generating creative copy, powering search results, or optimizing listings for SEO. That means providing rich text descriptions, high-quality images, videos, customer reviews, usage guidelines, sustainability certifications, and more. The more context AI has, the better it can craft engaging, accurate, and tailored content.

5. Centralized

Your product data shouldn’t live in 15 spreadsheets and a handful of legacy systems. To be usable by AI, data must be centralized in a single, trustworthy location, ideally a Product Information Management (PIM) system. Centralization eliminates version control issues, reduces duplication, and makes it easier to audit, update, and govern your data. It also ensures every AI application is drawing from the same source of truth.

6. Channel-Ready

Your product data must be flexible and adaptable. AI can help tailor content to each platform, whether that’s your eCommerce site, marketplaces like Amazon, print catalogs, or social media. But only if your data is already segmented and prepped for multichannel use. That means having different title lengths, formats, and tones for different channels, and language or region-specific versions when needed.

6 Questions to Determine if Your Product Data is Ready for AI

Before you dive into AI, it’s important to pause and assess the foundation you’re building on. Not all data is created equal, and not all data is ready for AI. These six questions will help you evaluate your current state and identify any red flags that could trip up your AI ambitions.

1. Do you have an established product data hierarchy?

Clear parent-child relationships (e.g., product families, variants) are essential for AI to understand your catalog structure. Without a clear hierarchy, AI might treat similar products as unrelated or miss opportunities to apply shared attributes. That leads to duplication, messy data, and irrelevant recommendations. A clean, logical structure is essential for training AI models to recognize patterns and apply rules efficiently.

2. Do you have at least 100 pieces of product data?

AI needs a critical mass of data to perform well. If you feed it too little, it won’t be able to detect patterns, test hypotheses, or generate meaningful results. Generally speaking, the more high-quality data you have, the smarter your AI becomes. This typically means 100+ well-documented, attribute-rich product records. Each should include structured fields like dimensions, materials, color, brand, and use cases. With a rich dataset, AI can make accurate inferences, generate tailored content, and even predict customer preferences.

3. Does all of your product data live in a single, centralized source?

Scattered data is one of the most common, and most frustrating, roadblocks to effective AI. If your product information is siloed across spreadsheets, outdated databases, DAMs (Digital Asset Management systems), or inside someone’s email inbox, AI won’t have access to the full picture. Worse, it may pull from inconsistent or conflicting versions of the truth. By storing your product data in a centralized system like a PIM platform, you create a single source of truth. That makes it easier to maintain, govern, and feed into AI applications. Centralization also reduces duplication, accelerates updates, and ensures that everyone (and every system) is using the same, up-to-date data.

4. Is your product data consistent and coherent?

If your data looks different from one product to the next, AI won’t know how to apply logic across your catalog. If one product is listed as “Large” while another says “L,” and another is “LG”, your AI model won’t recognize them as equivalent. That’s a recipe for bad recommendations, broken filters, and mismatched descriptions. Coherent data follows consistent naming conventions, formatting rules, and attribute structures. This uniformity allows AI to detect patterns, group similar products, and apply transformations or enrichments more effectively.

5. Does your product data have rich text fields?

Structured fields like specs and dimensions are important, but they’re only half the story. Rich text fields add depth and context. They include product descriptions, usage instructions, brand stories, SEO keywords, or care guidance. These fields provide the raw material for generative AI tools to create engaging content. Without rich text inputs, your AI won’t have enough “language” to work with. You might end up with generic, uninspired product copy or worse, AI-generated content that lacks accuracy or relevance. Rich text not only enhances the shopper experience, but it also helps AI create natural, persuasive, and informative product content at scale.

6. Is your product data localized for different markets?

If you sell internationally, localization is non-negotiable. Your product data needs to reflect the languages, cultural preferences, currencies, and regulatory standards of each region you operate in. AI tools can help automate translation, unit conversion, and channel-specific adaptations, but only if your data is set up to accommodate those needs. Localized data ensures that AI can produce relevant, compliant, and personalized content across geographies.

6 Tips for Creating a Strong Foundation of Product Information

If your answers to the above questions raised some red flags, don’t worry. Here’s how to get your data in shape:

1. Audit your existing data and technology: Start by evaluating the current state of your product data. What’s missing? What’s inconsistent? Which systems are involved? This gives you a roadmap for where to focus.
2. Identify key internal and external stakeholders: Involve product managers, marketing, IT, customer support, and compliance early. Everyone touches product data at some point, and their input will be vital to success.
3. Establish a clear and consistent taxonomy: Create a standardized naming and classification system for your products. This helps both humans and AI understand how products relate to each other.
4. Create a single source of truth for product data: Invest in a PIM or other centralized system where all product information lives. This ensures consistency across teams, regions, and sales channels.
5. Set up processes for translation and localization: Make sure your data can flex to serve multiple languages, units of measurement, and cultural nuances. AI tools can help, but they need clean inputs to get started.
6. Establish on-going data governance policies: Treat product data like a living asset. Set rules for how it’s created, reviewed, and maintained, and assign owners to ensure accountability over time.

Smarter AI Starts With Stronger Data

AI can be a powerful ally in managing, enriching, and scaling your product information. But it’s not a magic wand, it’s a multiplier. If your data is strong, AI will help you move faster, reach further, and deliver better product experiences. If your data is weak, AI will only amplify the gaps.

That’s why investing in clean, complete, and consistent product data is a strategic move. It lays the groundwork for automation, personalization, and innovation that actually work.

So before you dive into the latest AI tools, take a moment to look at the foundation you’re building on. Ask the right questions. Fill in the gaps. Organize what you have. With the right data in place, you’ll be ready to unlock the full potential of AI and turn it into a true competitive advantage.